Mineral Dust Cycle

Summary and Keywords

There is scientific consensus that human activities have been altering the atmospheric composition and are a key driver of global climate and environmental changes since pre-industrial times (IPCC, 2013). It is a pressing priority to understand the Earth system response to atmospheric aerosol input from diverse sources, which so far remain one of the largest uncertainties in climate studies (Boucher et al., 2014; Forster et al., 2007). As the second most abundant component (in terms of mass) of atmospheric aerosols, mineral dust exerts tremendous impacts on Earth’s climate and environment through various interaction and feedback processes. Dust can also have beneficial effects where it deposits: Central and South American rain forests get most of their mineral nutrients from the Sahara; iron-poor ocean regions get iron; and dust in Hawaii increases plantain growth. In northern China as well as the midwestern United States, ancient dust storm deposits known as loess are highly fertile soils, but they are also a significant source of contemporary dust storms when soil-securing vegetation is disturbed. Accurate assessments of dust emission are of great importance to improvements in quantifying the diverse dust impacts.

Dust is a dominant component of the total atmospheric aerosol burden. Most dust particles are lifted into the atmosphere by aeolian (wind) erosion processes of arid and semi-arid lands, which cover approximately 33% of the global land area. With an average lifetime of up to several weeks, dust (or mineral particles) can be transported over large distances downwind from the source. Each year, large plumes of dust are transported from the sources in North Africa across the Atlantic Ocean, reaching the Caribbean and the southeastern United States, as well as across the Mediterranean into Europe. Large quantities of dust, originating in Central Asia, are regularly carried out over the North Pacific to the West Coast of the United States.

Dust particles are not only natural phenomena, but they are also produced as a result of human activities. Various human activities can extend the geographical area of dust sources and increase the dust loading into the atmosphere. This portion of dust is called anthropogenic dust, and it is of special interest in climate change studies. Some estimates show that the anthropogenic fraction of dust could be as much as 30% to 50% of the total dust production.

Properties of Dust Particles

Mineral dust aerosols have complex nonspherical shapes and varying mineralogical composition. Particle sizes exhibit a wide size range, from about 0.01 to 100 μ‎m. The airborne mineral aerosol is a mixture of various constituents, the abundance of which could vary depending on the place of origin and dust mobilization processes. Some major dust aerosol components are quartz, clays (mainly illite, kaolin, and montmorillonite groups), carbonates (calcite, dolomite) and sulfates (gypsum). Also, some amount of organic material and soot could be present, especially in soil-derived (non-desert) aerosols. Various mineralogical compositions determine the microphysical and optical characteristics of dust aerosol and hence control the effects of dust on climate and environments. One key component is an amount of iron oxide that controls the ability of mineral dust to absorb sunlight. The composition of dust determines the overall radiative impact of dust, that is, cooling or heating of the climate system. Dust particles of different sizes and composition have different effects on climate, environments, and human health. For instance, the health impact is controlled by particle sizes, shapes, and composition.

Definition of a Dust Storm

Dust storm is defined as a phenomenon associated with lifting of dust particles from land surfaces into the air, resulting in a decrease of visibility. Dust storms are often characterized based on visibility observations, as well as satellite observations. A common definition used is that visibility less than 1 km corresponds to dust storms. There were several very famous dust storms with visibility less than 1 meter. For example, the Black Sunday Dust Bowl in 1935 in the Texas panhandle and Oklahoma panhandle had visibility less than 1 meter. A recent example is the dust storm that occurred in May 2013 in Antelope Valley, California, with a few centimeters visibility. The typical value of the dust concentration during dust storms can reach hundreds of μ‎g/m3.

The Sahara desert is a key source of dust storms, particularly the Bodélé Depression and an area covering the confluence of Mauritania, Mali, and Algeria. Saharan dust storms have increased approximately 10-fold during the half-century since the 1950s, causing topsoil loss in Niger, Chad, northern Nigeria, and Burkina Faso. In Mauritania there were just two dust storms a year in the early 1960s, but there are about 80 a year today. A second dust source area is located in Northern China, generating large dust storms each year.

Observations of Dust Storms

Traditionally, dust storm records are built on visibility observations. More recently, satellite observations have been increasingly used in studies of mineral dust. Much of our understanding of the large-scale dust distribution and transport rests largely on satellite remote sensing data. Dust plumes are readily observed in ultraviolet (UV), visible, and infrared (IR) channels of satellite instruments. Satellites provide the primary means of obtaining a global perspective of the areal extent of dust plumes as well as some dust properties such as optical depth and effective particle size.

Satellite passive instruments provide a column-integrated view of dust loadings in the atmosphere. Complementary, space-, and ground-based lidar observations allow for characterization of the vertical distribution of dust. Measurements reveal that dust plumes often exhibit a complex multilayered structure. For instance, transport of Saharan dust occurs at higher altitudes in a layer that typically reaches 5–6 km, although one or several layers might be present below it. Concentrations aloft are usually several times greater than in the marine boundary layer. Dust layers can be intermixed with layers of other aerosols or clouds.

Dynamics of the Dust Emission

Mineral dust aerosol is emitted from dry land surfaces under strong winds. The process of dust emission is a complex function of surface conditions and winds. Dust emission occurs when winds exceed a threshold wind velocity. The threshold friction velocity significantly varies in space and time, in response to soil moisture variability, surface roughness heterogeneity, and vegetation phenology.

As the source for mineral dust, global drylands encompassing hyper-arid, arid, semiarid, and dry sub-humid areas cover about 41% of Earth’s terrestrial surface and are home to over two billion people, or one-third of world population (Mortimore et al., 2009). Under a warmer and drier climate, the area extent of drylands has expanded since the mid-20th century, and is likely to continue expanding in the 21st century (Feng & Fu, 2013). Occurrences of drought conditions are likely to become more severe due to greenhouse warming, thereby creating more easily erodible dry and barren surfaces (Dai, 2011a, 2011b; Sheffield et al., 2012; Trenberth et al., 2014). Meanwhile, world population is projected to grow from 6.1 billion in 2000 by 47% to 8.9 billion in 2050, with most of the increase taking place in the less developed dryland areas (United Nations, 2004). Under the pressure of population growth, global drylands have undergone various forms of land-cover/land-use change (LCLUC) driven by policy, legislation, and institutional and development interventions, with 10−20% of drylands affected by varying degrees of land degradation or desertification (Reynolds et al., 2007). Transformations of rangelands and grasslands to cultivated lands, inappropriate irrigation practices, soil salinization and erosion, overgrazing, and deforestation are among the anthropogenic drivers that have caused significant changes to the areal extent and dust emission potential of the dust source areas (Lambin et al., 2001). While LCLUC is emerging as a fundamental element of climate change science due to its impact on the surface reflectivity and land-atmosphere interactions (Mahmood et al., 2010; Turner et al., 2007), the interconnections between LCLUC and mineral dust have not attracted much attention until recent years (e.g., Gutman et al., 2004).

Dust Microphysical and Optical Properties

The mineralogical composition and particle size are the major properties of mineral aerosol governing its impacts upon the Earth’s systems. Initially, the composition of soil grains and generation processes determine the particle size distribution of airborne dust particles, their composition, and the degree of particle aggregation. Both the particle size distribution and the composition can be altered during dust transport in the atmosphere, called dust aging.

The main species found in mineral dust are quartz, various clays (e.g., kaolinite, illite, montmorillonite), carbonates, feldspars, chlorites, and iron oxides (e.g., hematite, goethite), among others. These minerals are characterized by very different physical and chemical properties. For instance, different minerals have distinct abilities to adsorb water vapor and other chemically important atmospheric gases. Each mineral has distinct spectral optical constants (or refractive indices), which determine how dust particles scatter and absorb the electromagnetic radiation. Consequently, the properties of dust as a mixture are determined by the relative abundance of various minerals and their aggregates. However, numerous climate and remote sensing studies have considered dust as a single generic species. This is partly due to the complexity of quantitative determination of the mineralogical composition and a lack of data.

The dust composition is thought to vary from source to source. For instance, dust in the Sahelian region is characterized by a high Fe/Al ratio due to the abundance of ferralitic soils. In contrast, soils in the semi-arid regions of Central Asia contain less Fe. The difference in the amount of iron oxides is of special importance because they control primarily the ability of dust particles to absorb sunlight.

Dust particles cover a wide range of sizes from about a few tenths of micrometers to several hundreds of micrometers. Coarse particles are quickly removed from the atmosphere, and the ones transported over long distances usually have sizes below 20 μ‎m. Several size modes are commonly introduced to characterize the dust particle size distribution. Size modes may have different compositions. In general, clay particles tend to be smaller in size than those made of quartz or carbonates. Coarse dust particles (31–62 μ‎m) can travel up to a few hundred kilometers from their source, medium dust particles (16–31 μ‎m) can travel up to a few thousand kilometers, and fine dust particles (<16 μ‎m) can be transported globally.

Dust particles exhibit a large variety of shapes, often occurring as irregular (nonspherical) minerals or aggregates of several minerals. To date, there is no generally accepted classification of dust particle morphologies and data are limited. As a necessity, a simplified assumption that dust particles have spherical or spheroidal shapes is often made.

The concentration, composition, size distribution, and morphology of dust particles determine their optical properties. Dust particles can scatter and absorb electromagnetic radiation in a wide range of wavelengths from UV to IR. The optical characteristics needed for radiative effect assessments are the optical depth, single scattering albedo, and scattering phase function. These characteristics are also functions of the location and time, because of varying properties of transported dust.

The optical depth of dust plumes is largest near the dust source and it decreases farther from the source being controlled largely by the dust concentration. Over the oceans, the highest optical depths occur in regions influenced by dust transport. At visible wavelengths, optical depths as high as 10 have been measured during dust storms. It has a weak dependence on the wavelengths in the visible region, but various spectral features occur in the thermal IR. Observations of the dust optical depth in the thermal IR window region suggest that it is about 2–10 times smaller than in the visible.

The single scattering albedo, which is defined as the ratio of scattering and extinction coefficients, does not depend on particle concentration but rather on the particle composition, state of mixing, and sizes. It is a key optical characteristic that governs the heating or cooling impact of dust aerosols upon the Earth system. The single scattering albedo of dust is in the range from about 0.75 to 0.99 in the solar and is characterized by strong wavelength dependence, increasing from UV to near-IR. The single scattering albedo may vary during the transport as dust ages. If dust particles are coated by other aerosol species, they may have drastically different properties from those that are evident at the dust source.

Dust Particles’ Interactions with Clouds

There is mounting evidence that atmospheric aerosols interact with the climate system and impact the hydrological cycle through changes in cloud coverage, cloud properties, and precipitation (Colins et al., 1994; Levin & Cotton, 2008; Lohmann & Feichter, 2005). Certain aerosols can serve as cloud condensation nuclei (CCN) upon which water vapor can condense to form cloud droplets. This link between aerosol and cloud formation is referred to as the Aerosol Indirect Effect (AIE) and is a major source of uncertainty in climate change predictions. The Intergovernmental Panel on Climate Change (IPCC) in its report from 2007 (Figure 1), stated that aerosols potentially have a strong cooling effect due to AIE on climate that rivals the warming influence by the greenhouses gases.

Figure 1. Estimated global average radiative forcing from anthropogenic and natural sources. Also shown is the spatial scale and the level of scientific understanding. Figure obtained from IPCC, 2007 (Forster et al., 2007).

However, the level of scientific understanding was low, as indicated by an extremely large error bar. This uncertainty in AIE originates from poorly quantified assessments of aerosol-cloud interactions in climate models, on both global and regional scales. These challenges range from poorly understood interactions of different types of particulate matter with atmospheric water vapor, theoretical shortcomings in physical mechanisms that explain aerosol-water vapor interactions, and lack of observational data (on sources, emissions, etc.) to complexities associated with coarse grid size of global climate models. Measurements have played an important role in this research, and will need to span from regional to global scales in order to comprehensively test climate system models. Cloud droplet activation is the direct microphysical link between aerosols and clouds, and is at the heart of the aerosol indirect effect (Nenes & Seinfeld, 2003). Droplet activation in atmospheric models is often calculated from physically based prognostic formulations (e.g., Feingold & Heymsfield, 1992) that rely on Köhler theory (Köhler, 1936). Köhler theory considers curvature and solute effects on the equilibrium vapor pressure of a growing droplet and can describe the equilibrium growth of a droplet as a function of ambient supersaturation. However, it is well known that insoluble species like dust can serve as cloud condensation nuclei (CCN), giant CCN (GCCN) (e.g., Levin & Cotton, 2008; Rosenfeld et al., 2001), or ice nuclei (IN) (e.g., DeMott et al., 2003; Field et al., 2006), thereby affecting cloud microphysics, albedo, and lifetimes. Despite its well-recognized importance, assessing the impact of dust on clouds and climate remains uncertain due to theoretical shortcomings in current activation theory (Köhler theory) when applied to dust aerosol. Therefore, a thorough understanding of the interactions of mineral aerosol with warm clouds is of significant importance.

Because of differences in the parent soils, dust aerosol originating from different source regions can have different chemical composition. Formenti et al. (2010) suggested that the fraction of calcite content and the ratio of illite to kaolinite clay in mineral dust samples can be used as a fingerprint to identify dust from specific source areas. For instance, a ratio of illite to kaolinite mass fraction greater than five corresponds to dust from Northern Africa, while a ratio of illite to kaolinite mass fraction less than two is representative of dust originating from Eastern Asia.

During transport, dust particles (especially the carbonate fraction, which can constitute up to 30% of the total mass), provide reaction surfaces for heterogonous and multiphase reactions with anthropogenic pollutants such as nitrates and sulfates (Levin et al., 1996), resulting in modified dust properties, such as enhanced hygroscopicity (Hatch et al., 2008). Thus, differences in parent soils and emission and transport processes can cause substantial variability in size-resolved composition and morphology of dust particles (Jeong & Sokolik, 2007; Sokolik et al., 2001).

As particle size and shape are fundamental parameters that describe atmospheric lifetime, transport processes, as well as aerosol direct and indirect radiative impacts, uncertainties associated with mineral dust shape further complicates understanding of impacts of mineral dust on the Earth system.

Mineral dust has been found to play an important role in both warm (liquid) and mixed-phase clouds through both direct and indirect effects. When first emitted into the atmosphere, mineral dust particles are often insoluble, but during the course of their transport (short-, mid-, or long-range), they acquire some deliquescent material, such as (NH4)2SO4 (Levin et al., 1996), and become efficient Cloud Condensation Nuclei (CCN), upon which cloud droplets are formed through the process of activation. Changes in the CCN concentration affect the radiative properties of clouds, known as the “cloud albedo” or “Twomey” effect of aerosols (Twomey, 1974). The enhanced number of droplets is often accompanied by a reduction in their size, thereby affecting cloud precipitation efficiency. This may result in increased cloudiness, which gives rise to the so called “cloud lifetime” or “Albrecht” effect of aerosols (Albrecht, 1989). These effects combined are known as aerosol indirect effects.

The ability of dust particles to serve as a CCN under atmospherically relevant water vapor supersaturations depends on their mineralogy, size, morphology, and atmospheric processing (aging). Due to differences in chemical composition of the parent soils, as well as the emission and transport routes of suspended dust particles, mineral aerosol at the source region and downwind can have different chemical composition and morphology, which leads to differences in solubilities and hydrophilicities (Sokolik et al., 2001) with implications for dust CCN activation potential. Additionally, due to changes in precipitation patterns as a result of anthropogenic disturbances, the sizes of global arid regions are expected to increase by millions of hectares per year. All these factors combined makes mineral dust interaction with clouds even more complicated compared to other tropospheric aerosols. Thus, understanding the dust particle’s ability to act as CCN and associated impacts on clouds is essential for improved climate change predictions.

The inability of, and inconsistencies in, current droplet nucleation theories to accurately describe fresh and aged dust CCN activity makes mineral dust aerosol one of the least understood components of climate change. A number of models as well as modifications to the current activation theory known as Köhler theory (KT) (Köhler, 1936) have been proposed to describe insoluble particle CCN activity (e.g., Fletcher, 1958). Significant discrepancies, however, exist between reported laboratory measurements and results predicted by theories. This is because current activation theories, when applied to dust, assume that the dust CCN activity is dependent solely on the soluble fraction. Furthermore, all modifications to Köhler theory fail to account for the interactions between the hydrophilic insoluble fraction (or dust core) and water vapor, even if appreciable.

Predicting the complex effects of mineral dusts on clouds and climate requires integrating observational knowledge into theoretical descriptions or parameterizations. Laboratory studies can provide data for developing and constraining parameterizations for the use in numerical modeling of dust impacts on clouds. However, there are many challenges associated with laboratory measurements, such as generating mineral dust aerosol with a distribution that resembles the size distributions of dust plumes generated in the natural source regions, simulating atmospherically relevant water vapor supersaturations in laboratory instruments, accurately relating the composition of mineral dust CCN to its CCN activation potential, and identifying the role of dust processing and transformation in the atmosphere as a consequence of long range transport, to be able to encapsulate all of this information for use in climate models.

Hence, more work is desired with regard to the activation of mineral aerosol to simulate realistic dust-cloud interactions for accurate assessment of the dust aerosol indirect effect. The influence of dust particles on warm clouds can be more significant if they are large enough to act as a giant CCN (GCCN) (defined as particles with a dry diameter larger than 5 μ‎m), thus altering precipitation efficiency (i.e., promoting or suppressing precipitation).

Inconsistencies in size definitions of GCCN (Feingold et al., 1999; Levin et al., 1996) as well as confusion on the ability of pristine or aged dust to act as GCCN (Yin et al., 2002) complicate the current understanding of mineral dust and subsequent impacts on the climate and the hydrological cycle.

The Role of Human Activities in Dust Production

There is compelling evidence that dust activity is enhanced by human-induced land-cover/land-use change (LCLUC) around global source areas. Mulitza et al. (2010) demonstrated a sharp increase in dust emission and deposition caused by the development of commercial agriculture in the Sahel region since the beginning of the 19th century. Neff et al. (2008) suggested that the drastic increase in aeolian dust deposition in the western United States was caused by rapid expansion of agricultural and grazing activities during the 19th century. Excessive cultivation and poor farming practices contributed to the Dust Bowl catastrophes over the Great Plains in the 1930s and the Soviet virgin lands in the 1960s (Cook et al., 2009; Goudie & Middleton, 1992). Over-irrigation and resultant drying of the Aral Sea created an active dust source with an increasing trend of dust storm frequency in the past few decades (Indoitu et al., 2012).

The importance of human activities to regional and global dust budgets has promoted growing efforts to incorporate human land use in coupled dust-climate models to quantify the proportion of anthropogenic dust (Ginoux et al., 2010; Ginoux et al., 2012; Mahowald & Luo, 2007; Tegen & Fung, 1995; Tegen et al., 2004; Yoshioka et al., 2005). Mineral dust has been accounted for in recent Intergovernmental Panel on Climate Change (IPCC) reports as an important anthropogenic climate forcing agent, thereby overturning the conventional view of mineral dust as a natural aerosol (Boucher et al., 2014; Forster et al., 2007). According to Zender et al. (2004), anthropogenic dust can result from either direct land use disturbance or indirect modifications of climatic factors and land surfaces due to non–land use activities, such as greenhouse gas emissions. Although there is no clear definition of the anthropogenic proportion of total dust, it is widely acknowledged that agriculture (cultivation and grazing) and surface water body change are two of the most important sources of anthropogenic dust (Ginoux et al., 2010; Ginoux et al., 2012; Mahowald & Luo, 2007; Tegen & Fung, 1995; Tegen et al., 2004).

Model assessments of anthropogenic dust often rely on information of agricultural fractions and ephemeral water bodies to identify the location of potential human-made dust sources, such as the cultivation map of Matthews (1983) and the more recent agricultural fraction datasets by Ramankutty and Foley (1999) and Goldewijk (2001; Goldewijk et al., 2011). However, there continues to be disagreement on how to treat anthropogenic versus natural sources in dust models. Considering that land use disturbances tend to produce more erodible materials, Tegen and Fung (1995) used a higher emission factor for disturbed sources. Similarly, Tegen et al. (2004) assigned a lower erosion threshold velocity for agricultural lands. In contrast, Ginoux et al. (2012) prescribed a higher threshold velocity (10 m/s) to agricultural lands than natural source areas (6 m/s), in recognition of the vegetation-shielding effects over croplands and pastures. These methods are highly empirical and subjective, as they tend to tune the model response to the addition of anthropogenic sources, in order to reconcile the discrepancies between the modeled dust burden and observations. As a result, the inferred anthropogenic dust proportion strongly depends on the model and observation data, and therefore remains largely uncertain. For instance, Tegen and Fung (1995) forced the modeled dust aerosol optical depth (AOD) to match the observed AOD seasonality from Advanced Very High Resolution Radiometer (AVHRR), and found that disturbed sources contributed 20–50% to the total dust loading. Tegen et al. (2004) tuned the modeled dust emission to dust frequency records from ground stations, based on which they found less than 10% of global dust is contributed by human land use. Yoshioka et al. (2005) found that adding 20–25% dust from disturbed sources improved the model comparison with the Total Ozone Mapping Spectrometer (TOMS) aerosol index over North Africa. There are a few important caveats in these model estimates that are based on 3D dust concentrations or optical properties. The modeled 3D dust fields are subject to many sources of uncertainties from parameterizations of dust emission, entrainment, transport and removal processes, as well as radiative transfer processes. Although tuning the strength of human-made dust sources improves the model comparison against observations, such top-down model estimates of anthropogenic dust are not robust and may be invalid, because of the intervolving model and data uncertainties. Hence, it is desirable to adopt an alternative bottom-up approach by focusing on accurate simulations of dust emission processes, including the total amount and spatiotemporal distributions, from both natural and anthropogenic source areas. This can only be achieved based on a process-level understanding of dust emission dynamics and variability.

Apart from human land use, dust emission is governed by multi-scale climate variations. Dust storms are known to be a seasonal phenomenon in desert regions worldwide, where the source activity and dust transport follow the seasonal shift of atmospheric circulation, precipitation, and vegetation phenology. As the world’s largest dust source, North Africa is characterized by a winter peak from the southern Sahara and Sahel driven mainly by northeasterly harmattan winds, and a summer peak with strong contributions from the Bodele Depression and western Sahara associated with a number of factors, such as low-level jets, African easterly waves, haboobs, and dust devils (Knippertz & Todd, 2012). East Asia dust season peaks in late winter and spring due to the frontal cyclonic activities from Mongolia and northern China, while the dust activity weakens in summer due to weaker winds and increased vegetation cover (Shao & Dong, 2006; Zou & Zhai, 2004). It remains a challenging task for dust models to reproduce the observed annual dust cycle, because of insufficient representations of some key atmospheric processes, such as subgrid-scale wind variability and dry convection, and the controlling factors in dust emission, such as soil moisture and vegetation (Cakmur et al., 2004; Heinold et al., 2013; Klose & Shao, 2012; Knippertz & Todd, 2012; Shinoda et al., 2011). Additional complexity stems from representing the seasonal change in soil erodibility in response to the crop cycle, irrigation, residual management, livestock burden, and grazing pattern (Webb & Strong, 2011).

Processes Affecting Dust Emission and Their Representation in Models

Drylands are composed of diverse landscapes, including arid and semiarid deserts, temperate grasslands, shrublands, savannas, and agricultural lands. These regions experience seasonal wind erosions regulated by seasonal dynamics in atmospheric circulation, soil moisture, and vegetation, as well as by human land use (Shinoda et al., 2011). For instance, dust outbreaks in East Asia occur most frequently during spring, due to strong winds, dry soils, and low vegetation cover, whereas dust activity is inhibited by growing vegetation during summer (Kimura et al., 2009; Zou & Zhai, 2004). Despite less frequent strong winds in the 2000s than in the 1990s, the dust frequency has increased over the grasslands and cultivated lands in East Asia, suggesting that a decrease in the threshold velocity u*th due to weakened vegetation protection may have increased the dust activity (Kurosaki et al., 2011). In contrast to natural vegetation, agricultural lands undergo seasonal changes in soil erodibility, in response to crop cycle and pasture management (Webb & Strong, 2011).

Mineral dust particles are produced primarily through disintegration of soil aggregates following creeping and saltation of coarse soil grains over the surfaces (Shao, 2008). The magnitude of dust emissions depends on the meteorological conditions, particularly the surface friction (shear) velocity that is required to exceed a threshold value, called the threshold friction velocity, which in turn depends on the land surface state and properties (Shao, 2008). Several dust emission schemes have been developed and incorporated in weather and climate models to account for the dependence of erosion threshold on surface characteristics (e.g., soil moisture, aeolian roughness) and the relationship between the size-resolved dust vertical flux and the saltation and sandblasting processes (e.g., Fécan et al., 1998; Marticorena & Bergametti, 1995; Raupach et al., 1993; Shao et al., 1996). Compared to the physically based parameterizations, simplified dust schemes mostly used in General Circulation Models (GCMs) employ a fixed threshold velocity and directly compute the dust vertical flux as a power function of wind velocity (e.g., 10-m wind) (Tegen & Fung, 1995; Tegen & Miller, 1998; Uno et al., 2001). Depending on what dust scheme and meteorological data are used, estimates of dust emission strength from major source areas differ substantially, leading to a large spread of global annual emissions from 1000 Tg to 4000 Tg (Cakmur et al., 2006; Huneeus et al., 2011; Textor et al., 2006; Zender et al., 2004). Large model discrepancies are also found for dry and wet depositions. Textor et al. (2007) revealed that harmonization of the aerosol emission sources only had a minor effect on the aerosol burden and optical properties, which are constrained by satellite observations. This finding implies that any improvements to the representation of dust emission processes can further improve estimates of dust loading and deposition, highlighting the importance of physically based parameterizations derived from process-level understanding of dust emission mechanisms. So far, several dust model inter-comparisons have been conducted to evaluate model performances, reconcile the model differences, and pinpoint the potential need for improvements (Huneeus et al., 2011; Todd et al., 2008; Uno et al., 2006). However, the differences in model configuration, parameterizations, and input data among participating models are often too overwhelming to identify and rank the specific sources of model errors. In light of this problem, Darmenova et al. (2009) has developed a coupled dust modeling system, WRF-Chem-DuMo, comprising multiple dust schemes and the optimized input data for Asian dust sources, which can be used to systematically quantify the uncertainties associated with every model parameter and input data in physically based dust schemes. This modeling tool was used in a study conducted by Xi and Sokolik (2015) to assess an anthropogenic fraction of emitted mineral dust.

Impact of Dust Aerosol

Once lifted into the atmosphere, mineral dust aerosols cause a diverse impact on the environment and climate. Figure 1 illustrates the diverse impacts that may be caused by dust.

There has been growing interest on the inter-annual and decadal variability and long-term trends of dust aerosol. Horizontal visibility and synoptic weather records are the longest continuous dust-related measurements at worldwide meteorological ground stations, while multi-sensor aerosol observations from space now provide over 30 years data of the aerosol amount and distribution, including from early-generation satellites such as TOMS and AVHRR, and more recent advanced instruments such as Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging and Spectroradiometer (MODIS). Based on visibility and synoptic weather codes, Mahowald et al. (2007) and Shao et al. (2013) found decreasing trends of the dust frequency over most desert areas since the 1980s, except for a positive trend in the Middle East. The decreasing dust trend in northern China is due to the weaker cyclonic activity in the region, and likely the weakening of Siberian High since the 1970s (Panagiotopoulos et al., 2005; Zhu et al., 2008). Using 13-year (1997−2010) SeaWiFS Aerosol Optical Depth,, Hsu et al. (2012) found a strong positive trend of dust emission and transport from the Arabian Peninsula and a negative tendency in the dust outflow from North Africa. Similar trends were also found in the MODIS AOD (Zhang and Reid, 2010). It is often difficult to establish robust long-term dust trends from satellites, because of the relatively short data length and the strong correlations between dust and inter-annual and inter-decadal climate variability, such as El Nino/La Nina-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). Complementing the dust-related observations, coupled dust-climate models have been used to generate climatology of dust emission, loading, and deposition that allow a more detailed analysis of dust-climate linkages (e.g., Gong et al., 2006; Hara et al., 2006; Mian et al., 2013; Ridley et al., 2014; Zhang & Christopher, 2003; Zhao et al., 2006). The modeled spring dust emission in East Asia was found to highly correlate with Asian polar vortex indices, which reflect the strength of cold air intrusion from the polar regions (Gong et al., 2006; Hara et al., 2006). Through teleconnections, ENSO also modulates the dust emission and transport patterns by affecting the westerly jet and East Asian winter monsoon. In particular, La Nina events create an anomalously strong Asian polar front and more dust outflow compared to El Nino years (Gong et al., 2006; Hara et al., 2006). Ridley et al. (2014) find that the decline in dust outflow from North Africa is caused by a decreasing trend in the surface winds, which accounts for over 60% of the inter-annual variability of the dust AOD. They suggest that the dust inter-annual variability does not depend on the vegetation cover change, which is likely due to a simple representation of vegetation in the dust scheme, or out-of-phase of vegetation change with surface winds. It is argued that a process-level understanding of dust emission physics and explicit representation in dust models are required to characterize the linkages between dust, climate, and land-cover/land-use change (LCLUC).

While the dust models are best constrained in simulations of dust loading thanks to the available satellite observations, dust emission and (dry/wet) deposition are subject to great uncertainties. Depending on what model and meteorological data are used, estimates of global dust emission vary from 1000 to 4000 Mt (million tons) (Cakmur et al., 2006; Huneeus et al., 2011; Textor et al., 2006; Zender et al., 2004). Textor et al. (2007) found that harmonization of dust emission sources had minor effects on the aerosol burden among seven global models, suggesting a compensation effect between dust emission and deposition. This implies that any improvements to the representation of dust emission processes can lead to further improvements in modeling the dust lifetime (e.g., transport, loading, and deposition). In order to do that, top priorities include developing and testing physically based dust parameterizations, evaluating model sensitivity and performance, and preparing the required soil and surface datasets at appropriate spatial scales. So far, several dust model inter-comparisons have been conducted to evaluate model performances, reconcile the model differences, and pinpoint the potential need for improvements (Huneeus et al., 2011; Todd et al., 2008; Uno et al., 2006). However, the differences in the model configuration, parameterization, and input data among the participating models are too overwhelming to identify and rank the specific sources of model errors. In light of this problem, Darmenova et al. (2009) has developed a coupled dust modeling system comprising several dust schemes and optimized input data for Asian dust sources, which is a powerful tool to quantify the dust emission uncertainties associated with meteorology and surface characteristics. Similar work is done by Kang et al. (2011) who conducted a comparison of three dust vertical flux parameterizations in the same host model (i.e., WRF). Both studies point out that the dust emission uncertainty is caused by insufficient representation of model physics and a lack of critical soil and land data.

Dust has a considerable impact on the chemical and biological processes occurring in land and ocean ecosystems. Light is a vital factor governing plant photosynthetic activities. By altering the photosynthetically active radiation (PAR, 400–700 nm), dust affects the terrestrial vegetation functioning, with implications for the carbon cycle. Deposited dust may supply nutrients to ecosystems. Soluble iron supplied by dust particles deposited in the oceans is essential for the growth of phytoplankton. The transport of micronutrients (such as phosphate and potassium) associated with dust has been recognized for its importance to terrestrial ecosystems.

The degradation of visibility and health problems caused by dust make it an important air-quality issue. Dust particles are toxic or can act as the transport mechanism for toxic species, which can be inhaled and pose a health threat.

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